Title :
A multilayer neural adaptive network as a model of nonlinear plants
Author :
Tsypkin, Ya Z. ; Aved´yan, E.D.
Author_Institution :
Inst. for Control Problems, Moscow, Russia
Abstract :
Summary form only given. A multilayer neural adaptive network is chosen as a model for complex nonlinear plants. The basic element of the neural network (NN) is the sigmoid perceptron. The tuning of NN weights is based on measurements of the vector input and output of a nonlinear plant, the output of which is affected by an additive random noise. Optimal convergence rate algorithms for tuning the NN weights have been obtained
Keywords :
adaptive systems; feedforward neural nets; nonlinear systems; random noise; additive random noise; loss function; multilayer neural adaptive network; nonlinear plants; optimal convergence rate algorithms; sigmoid perceptron; vector input measurement; vector output measurement; weight tuning; Adaptive systems; Multi-layer neural network; Neural networks; Nonhomogeneous media;
Conference_Titel :
Neuroinformatics and Neurocomputers, 1992., RNNS/IEEE Symposium on
Conference_Location :
Rostov-on-Don
Print_ISBN :
0-7803-0809-3
DOI :
10.1109/RNNS.1992.268623
